Lung adenocarcinoma is the most common lung tumor and is especially prominent in former and non-smokers. The differential diagnosis, classification, and prognostic stratification of lung adenocarcinoma have long been challenging problems. We have applied gene expression analysis to generate a provisional classification system for human lung adenocarcinomas. In this proposal, we intend to validate our classification approach, to develop gene expression based methods further for clinical translation, and to apply gene expression-based classification to a mouse lung adenocarcinoma model developed by our group. Specific aims are as follows: 1. To reproduce a gene expression-based classification for human lung adenocarcinoma in an independent data set. We will generate expression data for 250 clinically annotated adenocarcinoma specimens for 18,000 human genes. We will then analyze these data using unsupervised clustering approaches and determine whether the clusters are similar to those observed previously. 2. To validate or refute prognostic differences between lung adenocarcinoma classes. We will apply predictive models from our previous analyses to determine the lung adenocarcinoma classes and determine whether these models accurately predict outcome in the new, independent data set. 3. To apply a reverse-transcriptase polymerase chain reaction method for expression profiling to analysis of lung adenocarcinoma specimens, for both outcome prediction and class prediction, and to apply this method to fine needle aspirate specimens. 4. To compare K-ras mutant mouse models of lung cancer with human lung adenocarcinoma classes. We will perform gene expression analysis of mouse adenocarcinomas and compare these profiles to the human tumors. The first goal will be to determine whether the mouse tumors correspond to specific human adenocarcinoma classes. The subsequent goal will be to test the mouse adenocarcinomas for gene expression predictors of response to trial chemotherapeutic agents.